#kotlin#android#awt#compose#declarative_ui#desktop#gui#ios#javascript#kotlin#multiplatform#reactive#swing#ui#wasm#web#webassembly
Compose Multiplatform is a Kotlin-based framework by JetBrains that lets you build user interfaces for multiple platforms—iOS, Android, desktop (Windows, macOS, Linux), and web—using mostly shared code. It is based on Jetpack Compose for Android, so you can use similar APIs across platforms, speeding up development and ensuring consistent UI design. iOS support is in beta, web is in alpha, and desktop and Android are stable. You can also access native features like camera or maps easily. This helps you save time, reduce bugs, and create apps that work well everywhere with less effort.
https://github.com/JetBrains/compose-multiplatform
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Zeus New Pytorch Ecosystem Tool
Zeus is an open source toolkit for measuring and optimizing power consumption of deep learning workloads.
🖥Github
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Main channel: @repo_science
Coupons: @freecoupons_reposcience
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Park, Chanwook, Sourav Saha, Jiachen Guo, Hantao Zhang, Xiaoyu Xie, Miguel A. Bessa, Dong Qian, et al. 2025. “Unifying Machine Learning and Interpolation Theory via Interpolating Neural Networks.” Nature Communications 16 (1): 1–12.
https://www.nature.com/articles/s41467-025-63790-8
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A few cool ideas in this model.
Introducing Gemma 3n: The developer guide - Google Developers Blog
https://developers.googleblog.com/en/introducing-gemma-3n-developer-guide/
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There is this new lib called scale. One could compile CUDA code to use it on AMD GPU.
https://docs.scale-lang.com/manual/how-to-use/
I don't know who is more pissed off, NVidia or AMD.
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This repo is really nice.
yuanchenyang/smalldiffusion: Simple and readable code for training and sampling from diffusion models
https://github.com/yuanchenyang/smalldiffusion
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Google & USC benchmarked a prompt based forecasting method, and the results are amazing.
Cao D, Jia F, Arik SO, Pfister T, Zheng Y, Ye W, et al. TEMPO: Prompt-based Generative Pre-trained Transformer for time series forecasting. arXiv [cs.LG]. 2023. Available: http://arxiv.org/abs/2310.04948